In order to train the R-peak Detection Model, we collected the portion of the ECG signals with ground truth annotation of the R-peaks from the ECG-ID database. The continuous signals were divided into windows of 1024 samples, with a step size of 256 samples. Each windowed signal is composed of roughly three ECG beats. These windowed signals were considered as input to the R-peak detector model, while the output was a pulse train showing the location of R-peaks as `1' and `0' elsewhere. In order to apply deep supervision, as reported in [1]}, the pulse trains were subsampled accordingly. 80% of the data was used to train the model and the remaining 20% was used as validation data. The model was trained for 100 epochs, minimizing binary cross-entropy loss using the Adam optimizer [2]}.
